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School of Advanced Science (SAS)

The School offer. M.Sc. programs in Mathematics, Physics and Chemistry. It also offers core and elective courses for all PG Programmes.

M.Sc.Physics

To provide the highest level of knowledge at Masters level and encourage to apply it to solve the real-world societal problems through innovations.

  • Academia and Industries
  • Opportunity to pursue higher education (Ph.D.) in India and abroad
  • Teaching
  • Applied Statistical Methods
  • Research Methodology
  • Scientific English 
  • Soft Skills
  • SET Projects ( Science and Engineering Technology)
  • Master’s Thesis
  • Mathematical Physics
  • Classical Mechanics
  • Electromagnetic Theory
  • Quantum Mechanics
  • Statistical Mechanics.
  • Introduction to Solid State Physics
  • Nuclear and Particle Physics
  • Atomic and Molecular Physics
  • Basic Electronics
  • Advanced Solid state Theory
  • Nanomaterials and its applications
  • Optoelectronics
  • Laser and Fiber Optics
  • Bio Physics
  • Microwave Physics and Device Technology
  • Plasma Physics and Technology.
  • General Physics Lab-I
  • General Physics Lab-II
  • Optoelectronics Lab

M.Sc. Chemistry (Organic / Inorganic / Analytical / Pharma)

  • To divulge students in the frontier areas of chemistry
  • To enhance the employability skills of the students and prepare them for placement in R&D centres, top-ranked academic institutes and industries
  • To expose students in the research - based learning
  • To encourage students to be a part of active research groups and motivate to conduct independent research.
  • In R&D centres, Universities of advanced learning, Research Centre of Excellence, Industries, Multi-national companies
  • In QC / QA Departments of Chemical and Pharmaceutical industries
  • Process and Production Department of Chemical and Pharmaceutical industries
  • Advanced computations utilizing Bio-informatics and Chem-informatics
  • Mathematics
  • Scientific English
  • Foreign Language
  • Soft Skill
  • SET Conference (we have to expand this as Science and Engineering Technology)
  • Research Methodology
  • Master’s Thesis
  • General chemistry
  • Inorganic chemistry
  • Analytical chemistry
  • Organic chemistry
  • Physical chemistry
  • Inorganic chemistry
  • Analytical chemistry
  • Organic chemistry
  • Physical chemistry
  • General chemistry
  • NMR, EPR and Mass spectrometry
  • Bioorganic Chemistry
  • Chemistry of Natural Products
  • Green Chemistry
  • Polymer Chemistry
  • Intellectual Property Rights
  • Drug Design, Biophysical Chemistry
  • Organometallics and Industrial Applications
  • Nanomaterials
  • Computational Chemistry
  • Quantum Chemistry
  • Group Theory
  • Molecular Spectroscopy
  • Advanced Inorganic Chemistry
  • Materials Chemistry
  • Nanomaterials & Characterization Techniques
  • Inorganic Photochemistry
  • Advanced Organic Chemistry
  • Chemistry of Heterocyclic Compounds
  • Organic Synthesis & Methodologies
  • Photochemistry and Pericyclic Reactions
  • Advanced Inorganic Chemistry
  • Advanced Organic Chemistry
  • Advanced Physical Chemistry
  • Analytical Quality Control and Quality Assurance
  • Organic Synthesis and Methodologies
  • Materials Chemistry
  • Medicinal Chemistry
  • Analytical/Physical Chemistry Practical-I
  • Analytical/Physical Chemistry Practical-I
  • Analytical Chemistry Practical -III
  • Organic Chemistry Practical -I
  • Organic Chemistry Practical - II
  • Organic Chemistry Practical III
  • Analytical/Physical Chemistry Practical-I
  • Analytical/Physical Chemistry Practical- II
  • Inorganic Chemistry Practical –I (Synthesis and Characterization)
  • Inorganic Chemistry Practical – II(Synthesis and Characterization)
  • General Organic Practical
  • Inorganic Chemistry Practical -I (Synthesis of Inorganic Materials)
  • Inorganic Chemistry Practical - II (Synthesis of Inorganic Materials)
  • Inorganic Chemistry Practical III (Characterisation and properties measurements of Inorganic Materials)
  • Pharmaceutical Quality control and Quality assurance
  • Pharmacognosy and phytochemistry
  • Medicinal Chemistry
  • Medicinal Chemistry practical
  • Pharmacognosy & Phytochemistry practical

M.Sc (Data Science)

  • To become a skilled data scientist in industry, academia, or government.
  • To use specialist software tools for data storage, analysis and visualization.
  • To develop original ideas and solve complex problems based on advanced knowledge of the principles and methodologies of data science.
  • To integrate knowledge and handle complexity in the area of computer science and information engineering.
  • Data Analyst
  • Business Analyst
  • Data Visualisation Engineer
  • Internal Data Science Consultant
  • New roles in all sectors that are experiencing digital transformation
  • Matrix Theory and Linear Algebra
  • English for Science and Technology
  • Foreign Language
  • Soft Skill
  • SET Conference
  • Research Methodology
  • Master’s Thesis
  • Probability Theory and Distributions
  • Sampling Techniques
  • Statistical Inference
  • Regression Analysis and Predictive Models
  • Multivariate Data Analysis
  • Time series analysis and Forecasting
  • Big- Data Analytics
  • Database Systems: Design and Implementation
  • Algorithms: Design and Implementation
  • Machine Learning
  • Data Mining and Business Intelligence
  • Python Programming for Data Science
  • Programming for Data Science using R
  • Programming for Data Science using SPSS
  • Programming for Data Science using SAS
  • Programming for Data Science using MATLAB
  • Programming for Data Science using MINITAB
  • Design and Analysis of Experiments
  • Optimization Techniques
  • Statistical Quality Control
  • Stochastic Process and Applications
  • Reliability Theory and Survival Analysis
  • Queuing Theory and Network Analysis
  • Bio-Statistics
  • Actuarial Statistics
  • Artificial Intelligence
  • Spatial Data Analytics
  • Exploratory Data Analysis and Visualization
  • Fuzzy Statistics

M.Sc.( Computational Statistics and Data Analytics)

  • To train the next generation of statisticians with a focus on the field of data analytics.
  • The students will learn the principles and methods of statistical analysis and put them into practice using a range of real-world data sets.
  • To provide a unique and coherent blend of modern statistical methods together with the associated computational skills.
  • To use computational tools on problems of applied nature.
  • To offers training in modern statistical methodology, computational statistics and data analysis from a wide variety of fields, including financial and health sectors.
  • To learn advanced level of statistical knowledge and data analytical skills.
  • Data Analyst
  • Business Analyst
  • Data Visualisation Engineer
  • Data Science Consultant
  • Data Analyst roles in all sectors
  • Fundamentals of Mathematics
  • Linear Algebra and Applications
  • Real Analysis and Applications
  • Discrete Mathematics
  • Computational Thinking
  • Problem Solving with Object Oriented Programming
  • Basic English
  • Communicative English
  • Foreign Language
  • Environmental Studies
  • Ethics and Values
  • Lean Start-up Management
  • Introduction to Soft Skills
  • Introduction to Personal Skills
  • Fundamentals of Aptitude
  • Introduction to Business Communication
  • Reasoning Skill Enhancement
  • Soft Skill
  • SET Conference
  • Research Methodology
  • Comprehensive Examination
  • Master’s Thesis
  • Fundamentals of Statistics
  • Probability and Random Variables
  • Basic Statistical Methods
  • Distribution Theory
  • Sampling Techniques
  • Theory of Estimation
  • Testing of Hypothesis
  • Regression Analysis and Predictive Models
  • Design and Analysis of Experiments
  • Operation Research
  • Statistical Quality Control
  • Time Series analysis and Forecasting
  • Multivariate Data Analysis
  • Stochastic Processes and Applications
  • Reliability Theory and Survival Analysis
  • Statistical Methods for Data Mining
  • Econometric  Analysis
  • Algorithms: Design and Implementation
  • Big Data Analytics
  • Database Systems: Design and Implementation
  • Machine Learning
  • Python Programming for Data Science
  • Programming for Data Science using R
  • Programming for Data Science using SPSS
  • Programming for Data Science using SAS
  • Programming for Data Science using MATLAB
  • Programming for Data Science using MINITAB
  • Queuing Theory and Network Analysis
  • Non-Parametric statistics
  • Bio-statistics
  • Advanced Operation Research
  • Actuarial Statistics
  • Bayesian Inference
  • Total Quality Management and Six sigma
  • Statistics for Management sciences
  • Statistics for Financial Modelling
  • Inventory Models
  • Statistical Methods for Bio-informatics
  • Demography and Official Statistics
  • Statistical Process Control
  • Statistical Consulting
  • Statistics for Biological and Earth Sciences
  • Statistics for Social and Behavioural Sciences
  • Statistics for Research, industry and Community Development
  • Statistics for Forensic Sciences